Field of the Disclosure
The invention relates to an apparatus, a method and a system for monitoring, detecting and tracking body orientation and/or motion, including a fall or near fall event condition.
Related Art
Body orientation/motion monitoring and particularly fall monitoring is very useful to a wide range of subjects, from elderly persons to emergency workers to race horses to anthropomorphic robots. While the ability to detect a hard impact fall is important in all cases, additional information about “soft” falls, such as, for example, when a person is overcome by smoke and slowly sinks to the ground, or near falls, as in when a race horse stumbles, are also important events to detect. Prior fall detection and monitoring technology has taken many forms. Some previous inventions have employed accelerometers in a waist worn device to detect human falls. Although wrist worn devices with accelerometers are relatively successful at detecting some “hard” falls, which are defined as sudden and rapid descents that are usually associated with a high-g impact, these devices have numerous drawbacks, including, for example, difficulty detecting backward and lateral falls and an inability to sense the difference between a person lying down and a person who has fallen. Thus, these prior systems cannot be used when a person is at rest or when a person experiences a “soft” fall without an impact (e.g., a firefighter that is slowly overcome by smoke and slowly sinks to the ground). Furthermore, these systems may completely miss near fall events, such as, for example, when an elderly person trips and grabs onto a railing in order to avoid a fall or a stumble or gait change event that might precede a hard fall. In fact, prior work, such as, e.g., the commercially available system once described at <<http://www.dynamic-living.com/pers-info.htm#fall>> (now available at <<https://web.archive.org/web/20071009130404/http://www.dynamic-living.com/pers-info.htm>>) can include disclaimers like “The Fall Detector cannot differentiate between a fall and simply lying down to rest. You will need to remove it before you lie down and then put it back on when you get up.”
An example of a device presently available commercially that can monitor body orientation and movement patterns in free living subjects is the Intelligent Device for Energy Expenditure and Physical Activity (IDEEA) (Minisun, Fresno, Calif.), as described in Zhang K, Werner P, Sun M, Pi-Sunyer F X, Boozer C N, “Measurement of human daily physical activity,” Obes Res, Jan. 11, 2003 (1):33-40. This accelerometer based device is costly and requires the use of five sensors that are taped directly to the skin (e.g., chest, both thighs, and bottom of both feet) and associated cable tethers to attach the sensors to a waist worn data recording unit. The manufacturer claims that the IDEEA can identify various body postures and types of physical activity, including lying, sitting, walking, climbing stairs, running, and jumping.
Another example is the BodyTrac system (IMSystems, Baltimore, Md.), which includes a body posture and movement pattern recorder nominally worn as a chest band. The BodyTrack system employs a 5.8 cm×3.4 cm×1.7 cm module containing a sealed sphere with a bolus of mercury that, depending on body posture, short different sets of contacts. The BodyTrac system is claimed to provide the following body posture information: upright, walking, lying supine, lying right, lying left, and lying prone. A major limitation of the BodyTrac system appears to be an inability to discriminate between sitting down and standing up; to accomplish this, the BodyTrac system employs a second monitor that must be worn on a thigh, as described by Gorny S W, Allen R P, Krausman D T, Cammarata J., “Initial demonstration of the accuracy and utility of an ambulatory, three-dimensional body position monitor with normal, sleepwalkers and restless legs patients,” Sleep Med, March 2001; 2 (2):135-143.
Other currently available devices used to monitor falls, such as, e.g., described in Pervasive Computing, “A Smart Sensor to Detect the Falls of the Elderly,” Sixsmith, Andrew; Johnson, Neil Vol. 3, No 2 pp 42-47, include sophisticated visual monitoring systems and “smart homes” that feature sensors embedded in floors or infrared beams positioned near the floor.
Moving patterns have also been studied using different frequencies from sensor measurements. For example, K. Sagawa, T. Ishihara, A. Ina, H. Inooka, “Classification of Human Moving Patterns Using Air Pressure and Acceleration,” (IEEE—7803-4503-7/98) describes how slight change of air pressure can result from vertical movements and particular movement styles. Methods to calculate the relationships to specific moving patterns are also described. However, the human behavior described and tested includes walking or jogging, climbing up or down the stairs, and other high-g force movements that don't necessarily require high sensitivity sensors for pattern identification and thus have limited uses/functionality.
Improved movement pattern recognition and tracking has also been described to include complex systems. These complex systems have been described in O. Perrin, P. Terrier, Q. Ladetto, B. Merminod, Y. Schutz, “Improvement of Walking Speed Prediction by Accelerometry and Altimetry, Validated by Satellite Positioning,” Med. Biol. Eng. Comput., 164-168, 2000. These systems are described to utilize satellite positioning data to validate and provide an energy consumption calculation that takes into account ground inclination angles. The reliability and accuracy of the systems described continues to be limited however by the sensitivity of the sensors used to measure changes in acceleration, altitude, and areas suitable for GPS signals reception.
The development of sensors for the measurement and detection of moving patterns has also been an active area of research and development. For example, for measurements on rotating turbine blades, a semiconductor pressure sensor with improved sensitivity for a turbine environment has been described in D. A. Kurtz, R. W. Aisworth, S. J. Thorpe, A. Ned, “Acceleration Insensitive Semiconductor Pressure Sensors For High Bandwidth Measurements on Rotating Turbine Blades,” Presented at “XVth biennial Symposium on Measurement Techniques in Transonic and Supersonic Flown in Cascades and Turbomachines, Florence 2000. The semiconductor pressure sensor described includes one exposed “g-sensitive” pressure sensor positioned next to a covered “g-sensitive” pressure sensor to provide an acceleration insensitive pressure sensor. Improvements and other novel configurations for other environments and applications of this type have been highly desired.
As such, studies of human activity and measurements of metabolic rate to measure and determine activity patterns in different environments have been described. In Y. Ohtaki, M. Susumago, A. Suzuki, K. Sawaga, R. Nagatomi, H. Inooka, “Automatic classification of ambulatory movements and evaluation of energy consumptions utilizing accelerometers and a barometer,” Microsyst Technol (2005) 11: 1034-1040, energy consumption correlations to physical activity have been described to be suitable for systems to monitor and control health conditions. The systems described include a portable device attached to the waist of an individual that is able to provide a better estimation of physical activity over a conventional calorie counter. However, again the physical activities are limited to high-g force ambulatory movements promoted in health promotion programs.
More recently, methodological advances for specific human activities and environments have been described in T. Yamazaki, H. Gen-No, Y. Kamijo, K. Okazaki, S. Masuki, H. Nose, “A New Device to Estimate VO2 during Incline Walking by Accelerometry and Barometry,” Medicine & Science in Sports & Exercise, 0195-9131/09/4112-2213/0, 2009, and F. Bianchi, S. J. Redmond, M. R. Narayanan, S. Cerutti, N. H. Lovell, “Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 18, No. 6, December 2010. The T. Yamazaki et al. device is described as being able to take into account altitude changes to more reliably calculate VO2 consumption. The F. Bianchi, et al. device is described to implement the triaxial acceleration measurement as in T. Yamazaki combined with a barometric pressure sensor to detect events during falls. The functionality and reliability of these improved systems is limited to the sensitivity and response of the sensors implemented to measure the moving patterns and altitude changes. Accordingly, improved sensitivity of sensors for moving pattern recognition devices is highly desired.
Further, although some of these systems may provide autonomous notification aspects, none allow for the detection of near falls, and none would allow for the detection of falls both outside/inside of the subject's home due to their complexities and requirements. Therefore, these devices would not prove useful for emergency workers, individuals with seizures, race horses, anthropomorphic robots, or the like.
Thus, a long felt, unfulfilled need exists for a sensor device, system and method that may relatively innocuously monitor and detect a fall event type by a user, such as, for example, but not limited to, an emergency worker, an individual with a medical condition, a race horse, anthropomorphic robot, or the like.